Implied Volatility and Magnitude vs. Direction

Awhile back, a reader posed what sounded like a basic question:

Isn't implied volatility a function of the size of the move, rather than direction? So why is VIX more commonly referred to as a fear index if high VIX can imply movements in both directions? I mean- HOW and exactly where does the direction get factored into VIX?

This question sounds as if it if might have an easy answer. The problem is that when you scratch the surface of implied volatility, a bunch of other questions have a tendency to pop up and all the tangents make it easy for the questioner’s eyes to glaze over. So, to make a long story a little shorter, I have decided that it is time to draft some detailed posts that look under the hood at both implied volatility and historical/statistical volatility. Not to worry, this won’t happen today; more likely I will tackle these subjects over the course of the next two or three weeks in a series of posts.

Getting back to the questions posed above, to set the context, historical volatility (also known as statistical volatility) looks backward at actual historical price movements and calculates volatility in terms of standard deviations over a given period of time. The result is expressed as an annualized volatility number (the percentage of one standard deviation), just like the VIX. Historical volatility does not care about the direction of the volatility, only the magnitude.

In theory, implied volatility should also be directionally agnostic. Investor psychology and the mechanics of the options marketplace, however, mean that reality diverges from theory as options are transacted. Recall that implied volatility is derived from actual options prices. When the other options pricing variables (strike price; price of the underlying; time to expiration; dividends; and risk free interests rate) of an option are frozen, this leaves implied volatility as the only remaining variable in the options pricing equation. This is how implied volatility is derived.

In the very short term, options prices are largely a function of the price of the underlying and implied volatility, as the other variables tend to change at a slow and/or predictable rate. When the price of the underlying is relatively stable, implied volatility has a tendency to drift down and bring options prices with them. When the price of the underlying (whether it be a stock, ETF, index or whatever) moves sharply, this is when things get interesting. If the underlying moves up quickly, it has a tendency to attract new buyers and sellers. Implied volatility usually rises with the move and so do most options prices. Ultimately, implied volatility becomes a function of supply and demand for options at specific strikes and expirations. If new transactions keep hitting the ask price, then options prices will move quickly and implied volatility will adjust upward to accommodate the new prices. Sellers will also be inclined to keep raising their asking price to account for this demand – again stretching prices and pulling implied volatility along for the ride. Generally, this will not result in a “panic buying” situation, particularly if the underlying is an index or an ETF. Greed is not instantaneous; it tends to build over time. There might be a concern with an individual stock that an acquisition is in the works, a legal matter has been settled, word of an FDA decision has leaked out, etc., but other than these scenarios (which do not apply to an index or an ETF), sharp upward moves tend to be orderly and have a relatively limited short-term psychological impact on the investor.

If you turn this scenario around and think of the move as a sharp selloff, some different dynamics come into play. First consider the maxim that stocks tend to fall faster than they rise. Second, when it comes to portfolio protection, the rush to buy puts to protect an existing position is much more dramatic than any sort of call purchases during a bull spike. Third, the worst case scenario for a bear move includes not only the scenarios noted in the paragraph above, but incorporates any number of potential disasters from a CEO/CFO resignation to accounting irregularities, lowered earnings guidance, new legal challenges, etc. Fourth, as the downward move gathers momentum, investors have a tendency to buy whatever puts they can, at the market, for whatever prices are available. It is this insensitivity to prices during a panic selling situation that tends to overwhelm the ask price and cause market makers to raise the prices of puts dramatically; this, in turn, triggers a sharp jump in implied volatility.

Ultimately, the same aspects of investor psychology and behavioral finance (i.e., loss aversion) that translate into more panic selling than panic buying also mean that the supply and demand imbalance for options is typically greater in sharp bear moves than in sharp bull moves. The result is that implied volatility tends to spike more with an X% drop than as a result of an X% rise. Statistically, these moves are identical, but psychologically and from a transaction perspective, spikes down will generally move implied volatility more than comparably sized spikes up. Since the VIX is essentially the implied volatility of the SPX, this is one of the reasons why the magnitude and direction of a market move determine the impact the move will have on the VIX.

Natural Gas Implied Volatility Spiking

Perhaps it is just a coincidence that the “Oil VIX” appeared on the scene just as the implied volatility in oil futures (or at least as captured by USO) was hitting an eight month high. The “Oil VIX” (formally known as the CBOE Crude Oil Volatility Index; ticker OVX) and crude oil may get the lion’s share of the energy headlines, but lately it has been natural gas that has been making the more dramatic moves.

A look at the three month chart of UNG (the natural gas ETF that is the counterpart to USO), courtesy of the ISE, shows implied volatility steadily increasing over the past five weeks, with the gap between implied volatility and historical volatility continuing to widen – all while natural gas has pulled back about 27%.

Natural gas implied volatility levels are higher than oil implied volatility levels at the moment, and the pullback in natural gas presents some interesting trading opportunities. Some momentum players are already short here and some value hunters are buying on weakness, particularly if they believe in the long-term commodity bull and other supply and demand issues that are specific to natural gas.

The case for natural gas trading sideways from current levels is hard to make. Directional bets are expensive, due to high IV. Two trades I am looking hard at, with a bullish directional bias, are bull put spreads and call backspreads. The former limits upside and downside; the latter is more aggressive and more risky.

Implied Volatility at Fannie Mae (FNM) Tops 400

Not that this should surprise anyone who has been following this story, but it is an impressive number nonetheless and deserves to be captured here for archival purposes at the very least.

On Measuring Volatility

Mike at HEDGEfolios.com has a good post up today with the title of Measuring Volatility. He touches a lot of bases, but it all starts with the following statement:

“When it comes to measuring or sensing stock market volatility, I do not follow the VIX.”

Now I may have invented that silly tagline, “Your one stop VIX-centric view of the universe,” but I am the first to argue that a defaultist mind set is the wrong way to approach the investment landscape. If you follow the same indicators with the same default settings as everyone else, you are setting yourself up not just to follow the crowd, but to be a half step behind it. In order beat the crowd, what is needed is a variant perception.

Back to HEDGEfolios for a moment:

“The key element of volatility using traditional methods like the VIX rests on the reversal at extremes in a contrarian indication such as buying when the VIX exceeds 30. This is a very dangerous concept and I do not advocate for its use… I never liked that approach so I do my own thing and look at each stock, the turnover in each and how the composite of all signal changes indicates the market volatility.”

Volatility is a wide-ranging concept. It can be defined, measured and applied to over 10,000 stocks and ETFs in many different ways. To think that best way to harness information about volatility is to buy when the VIX hits X is ludicrous.

Consider that the concept of volatility can be applied not just to price, but to volume, options prices, market breadth data, etc. Volatility is a characteristic of every slice of the almost infinite flow of data that is associated with the markets.

It’s not just what you measure, it’s how you measure it. Volatility can look forward when it is in the form of a forecast or a derivation, such as implied volatility. When volatility looks backward, the opportunities to get creative are even richer. There is historical volatility, average true range, Bollinger bands, Chaikin volatility, relative volatility, and a variety of ways in which to index volatility.

Go ahead and watch the VIX, but don’t think for a moment that you are going to have an advantage over the thousands of other people who are watching the same indicator. Sure, you might come up with the next great VIX permutation, but you are far more likely to get a leg up on the competition by revisiting some basic questions:

• Is volatility worth following?
• How can more knowledge about volatility make me a better investor?
• Which aspects of volatility should I pay attention to?
• How should I measure that type of volatility?
• How do I interpret those measurements for maximum ROI?

One of my favorite measures of volatility is the number of buy and sell signals my various systems generate each evening. It’s simple, but effective. And I can be sure that nobody is not going to show up on CNBC tomorrow touting the same approach.

Volatility at RKH Regional Banking ETF

I just mentioned RKH, the HOLDRS regional banking ETF, on Wednesday in Regional Banking Woes Worsen, where I also posted a graph of RKH’s six month stock price and 30 day implied volatility.

I am revisiting RKH today with a slightly different chart, also courtesy of the ISE, that compares RKH’s implied volatility and historical volatility over the past six months. In the chart below, notice that the implied volatility in this ETF has been more of an uptrend than the spike from mid-March. Notice also that implied volatility appears to have hit a plateau and is still far below the mid-March peak.

Also of interest, the gap between implied and historical volatility is at a six month high right now, as historical volatility has been trending down over the past three weeks at the same time implied volatility has been trending up.

Finally, note that in those instances where historical volatility has been elevated, it usually preceded a drop in implied volatility; conversely, where historical volatility has been depressed, this has a tendency to precede an uptick in volatility. Volatility extremes often signal turning points. Whether the current high implied volatility and low historical volatility means that the regional banks are finally bottoming remains to be seen, but I have to believe that the probability of a bottom is increasing with each volatile session.

Ten Things Everyone Should Know About the VIX

I have had quite a few requests to present some introductory material on the VIX, so with that in mind I offer up the following in question and answer format:

Q: What is the VIX?
A: In brief, the VIX is the ticker symbol for the volatility index that the Chicago Board Options Exchange (CBOE) uses to calculate the implied volatility of options on the S&P 500 index (SPX) for the next 30 days.

Q: How is the VIX calculated?
A: The CBOE utilizes a wide variety of strike prices for SPX puts and calls to calculate the VIX. In order to arrive at a 30 day implied volatility value, the calculation blends options expiring on two different dates, with the result being an interpolated implied volatility number. For the record, the CBOE does not use the Black-Scholes option pricing model. Details of the VIX calculations are available from the CBOE in their VIX white paper.

Q: Why should I care about the VIX?
A: There are several reasons to pay attention to the VIX. Most investors who monitor the VIX do so because it provides important information about investor sentiment that can be helpful in evaluating potential market turning points. A smaller group of investors use VIX options and VIX futures to hedge their portfolios; and an even smaller bunch use those same options and futures to speculate on the future direction of the market.

Q: What is the history of the VIX?
A: The VIX was originally launched in 1993, with a slightly different calculation than the one that is currently employed. The ‘original VIX’ (which is still tracked under the ticker VXO) differs from the current VIX in two main respects: it is based on the S&P 100 (OEX) instead of the S&P 500; and it targets at the money options instead of the broad range of strikes utilized by the VIX. The current VIX was reformulated on September 22, 2003, at which time the original VIX was assigned the VXO ticker. VIX futures began trading on March 26, 2004 and VIX options followed on February 24, 2006.

Q: Why is the VIX sometimes called the “fear index”?
A: The CBOE has actively encouraged the use of the VIX as a tool for measuring investor fear in their marketing of the VIX and VIX-related products. As the CBOE puts it, “since volatility often signifies financial turmoil, [the] VIX is often referred to as the ‘investor fear gauge’”. The media has been quick to latch onto the headline value of the VIX as a fear indicator and has helped to reinforce the relationship between the VIX and investor fear.

Q: How does the VIX differ from other measures of volatility?
A: The VIX is the most widely known of a number of volatility indices. The CBOE alone recognizes nine volatility indices, the most popular of which are the VIX, the VXO, the VXN (for the NASDAQ-100 index), and the RVX (for the Russell 2000 small cap index). In addition to volatility indices for US equities, there are volatility indices for foreign equities (VDAX, VSTOXX, VSMI, VX1, MVX, VAEX, VBEL, VCAC, etc.) as well as lesser known volatility indices for other asset classes such as currencies.

Q: What are normal, high and low readings for the VIX?
A: This question is more complicated than it sounds, because some people focus on absolute VIX numbers and some people focus on relative VIX numbers. On an absolute basis, looking at a VIX as reformulated in 2003, but using data reverse engineered going back to 1990, the mean is a little bit over 19, the high is just below 50 and the low is just below 10. Just for fun, using the VXO (original VIX formulation), it is possible to calculate that the VXO peaked at about 172 on Black Monday, October 19, 1987.

Q: Can I trade the VIX?
A: At this time it is not possible to trade the cash or spot VIX directly. The only way to take a position on the VIX is through the use of VIX options and futures. It is possible that at some point there will be a VIX ETF or a VIX ETN, but no such products have been announced.

Q: How can the VIX be used as a hedge?
A: The VIX is appropriate as a hedging tool because it has a strong negative correlation to the SPX – and is more than four times more volatile. For this reason, portfolio managers often find that buying of out of the money calls on the VIX to be a relatively inexpensive way to hedge long portfolio positions. Similar hedges can be constructed using VIX futures.

Q: How do investors use the VIX to time the market?
A: This is a subject for a much larger space, but in general, the VIX tends to trend in the very short-term, mean-revert over the short to intermediate term, and move in cycles over a long-term time frame. The devil, of course, is in the details.

What Is High Implied Volatility?

Readers generally ask much better questions than my rhetorical ones. For that reason, I will see if I can set aside more of my time in this space to answer reader questions.

Recently I received several questions about how to determine whether implied volatility is high and when someone declares IV to be high, what exactly the basis for comparison is.

Ultimately, the assessment of what is high implied volatility is a subjective one, but typically the person attaching the label is making a comparison between current implied volatility levels and a historical range of either implied volatility or historical volatility levels.

To my thinking high implied volatility is best determined according to the following criteria:

1. relative to a lookback period (1 year, 6 months, 3 months or whatever – but be careful with shorter time frames) for previous implied volatility levels
   
   
2. relative to historical volatility (one could argue that the time frames are less important here)
   
   
3. relative to the current implied volatility of peers or a broader group of similar stocks (you hardly ever hear about this, but I think the comparison is relevant)

The most important piece of information to remember is that implied volatility is inherently forward looking and historical volatility is, by definition, backward looking. This is important because traders know when potential market moving events are coming and implied volatility moves accordingly. With historical volatility, on the other hand, it is much too easy to drive a car right into an unseen wall while trying to navigate by looking out the rear view mirror.

When I think about implied volatility levels, I am usually looking at ‘relative volatility’ or implied volatility as a % of the most recent 52 week range. The recent volatility trend, if any, is also worth investigating. I always check the current IV-HV spread, but to switch metaphors, it is generally better to know about the hurricane headed your way than the one that has just passed through. Finally, it is critical to know if important events are just around the corner, such as earnings, an FDA decision, the resolution of important litigation, etc. When evaluating implied volatility for ETFs or indices (and data sensitive stocks, such as financials), upcoming events to focus on would likely be more along the lines of impending government data, proximity to Fed meetings, etc.

For the record, I went back and coded some of the more important archived posts on implied volatility with the implied volatility label. In the future, I will offer up some thoughts on how to interpret implied volatility levels and use some real-time examples.

Implied Volatility Suggests Risk in Financials at Six Month Low

Just yesterday, in Financials Struggle to Establish Momentum, I expressed some concern that the recent relatively weak performance of the financial sector (XLF) did not bode well for any sustainable bull moves. Perhaps the sector overheard me, as today the XLF is up 2% in an otherwise flat market as I type this.

While the price action is ultimately what matters most, there is more to the story than just the prices of the financial stocks. In particular, I am watching the implied volatility of XLF, the financial sector’s bellwether ETF. As depicted in the chart below, the implied volatility (which has a significant fear and anxiety component in it) for XLF is approaching levels not seen since the first week in November.

I consider option traders to be a fairly savvy bunch; if they think that the risk premium in the financial sector is lower than any time in the past six months, I am going to listen – and watch to see what happens to the price.

Citigroup and Implied Volatility

Consider for a moment that much beleaguered Citigroup (C) is going to report earnings tomorrow morning on options expiration day.

Shouldn’t implied volatility tell us something about how anxious investors should be? Well, that is indeed the case, but the story is not perhaps what one might expect. If implied volatility is to believed, anxiety about Citigroup’s plight peaked about a month ago (when the stock market bottomed) and has been declining ever since. At current levels, Citigroup’s implied volatility is at the lowest level it has been in the past six weeks.

I have included Citigroup’s three month implied volatility chart below, as it is too interesting to let this one go and should make nice fodder for the archives.

Watching XLF Price and IV Action

The financial sector stock de jour is BSC today – unless of course it’s WM. With anxiety over ABK and MBI seemingly on the back burner at the moment and TMA and LEH suddenly so yesterday, which financial stock is going to set the tone for the market? My take is that since one tipping domino is unlikely to be contained, I am continuing to focus on the broad sector ETF, XLF.

Just a week ago today, I posted a one year chart of XLF showing price and implied volatility; in the interim, a great deal has changed, with the bears and the news flow dictating the action. From a technical standpoint, just yesterday XLF took out the 52 week high (47.99) in implied volatility from November 8^th and the 52 week stock price low (24.11) from January 22^nd. The chart below zooms in on the last six months and shows that yesterday’s selloff generated a new stock price low of 23.50 and IV high of 54.52 – a fitting tribute to the eight year anniversary of the NASDAQ all-time high.

Going forward it is important to watch the action in newsmakers like BSC and the other financial headline makers, but XLF is an excellent proxy for the sector and a good way to keep focused on the forest instead of the trees. Note also the action in the XLF calls for March, April and June, which shows some significant bets are being made that today’s XLF-led market bounce will not be a one day affair. No matter how things play out, XLF should be an excellent tell. I suggest you watch it as closely as you do the broader indices and keep an eye not just on the price, but also on the IV and put to call activity.

ISE Implied Volatility Charts

When it comes to implied volatility charts, I normally use the charts from my two favorite options brokers: thinkorswim and optionsXpress. On the other hand, this blog is littered with IV charts from iVolatility.com, largely because these charts are freely available on the web and because the look and feel is relatively clean and uncluttered.

For a visual change of pace, I suspect I will soon start posting some of the excellent thinkorswim charts, but for those wishing to roll their own, I want to offer a strong recommendation for the implied volatility charts put out by the ISE. When it comes to the ISEE charts on the ISE site, I am often frustrated by the poor graphics, but the ISE charts for individual securities are excellent. An example of one of the ISE’s volatility charts is the one I have included for XLF below. These charts can be customized to a time frame of 3 months, 6 months or 12 months and allow users to specify, via check boxes, any of stock price, implied volatility, and 30 day historical volatility (I have historical volatility turned off here.) As you can see from the graphic below, there is a lot of information crammed into these charts, including daily stock and option volume (easier to read in the shorter time frames), as well as a fair amount of volatility data. All data is delayed by 20 minutes, but as far as I am concerned these are the best free volatility charts out there.

To generate your own volatility charts at ISE, try their Quotes/Volatility page.

MBI, Bond Insurers, and Volatility

One of the more interesting – and important – subplots to keep an eye on during the current market difficulties is that of the bond insurers. The two most prominent of these bond insurers, MBIA (MBI) and Ambac (ABK), are in the news today with reports that the New York Insurance Superintendent is trying to arrange a capital infusion from the likes of Goldman Sachs (GS), Merrill Lynch (MER), JPMorgan (JPM), Citigroup (C), and Wachovia (WB). Presumably, the Fed is doing some arm twisting and offering some financial incentives behind the scenes, as a failure to resolve the problems with the bond insurers would likely trigger systemic havoc and involve a long and expensive list of dominoes in the process.

Eric Dinallo, the New York Insurance Superintendent, was quoted earlier today as saying that while a rapid resolution is essential, ironing out the details of a bailout may take awhile. “It is important to resolve issues related to the bond insurers as soon as possible,” Dinallo noted, while cautioning “these are complicated issues involving a number of parties and any effective plan will take some time to finalize.”

While most investors should be thinking about the bond insurer issue in terms of its impact on the broader markets, there are some interesting plays on bond issuers themselves. As reported in 24/7 Wall Street, Goldman Sachs laid out some potential valuations under three different scenarios, ranging from the bond insurers’ being unable to raise enough capital to mollify the rating agencies to a situation where the capital raised enables the bond insurers to continue to operate as they had in a pre-crisis mode. Looking just at MBI, the valuation spread ranges from $6 to $48.

Investments don’t get much more speculative than this, as the chart from optionsXpress above shows. For the record, all February puts now carry an implied volatility of more than 200. While I am not going to recommend a specific trade here, there are some fascinating options spreads and ratio spreads to look at for those who believe that the Goldman scenarios and numbers are in the ballpark.

Implied Volatility as a Sector Drill Down Diagnostic

I have said relatively little about the crisis in the financial sector largely because there are so many others out there who are covering this story in much more detail than I have any desire to get into. Also, my trading is driven largely by technical analysis, charts and market sentiment, with fundamental analysis usually playing a prominent role only in my long-term holdings.

That being said, this blog has an emphasis on volatility and risk, so this morning I pulled up some implied volatility charts in the financial sector and drilled down from general to specific to see to what extent implied volatility might indicate vis-à-vis the possibility of the tide turning in investor fear. I have appended several of these charts below. On the left hand side, they include the generic large cap financial sector index, XLF (components), as well as the securities broker dealer index, XBD, whose volatility I analyzed back in August. On the right side, I have the banks. The BKX (components) is capitization-weighted and thus tilts toward money center banks; the KRX (components) has a strong regional and local focus; and the MFX (components), as the name suggests, includes banks and other financial companies that are heavily involved in the mortgage finance business. For comparison purposes, the BKX is down 18.7% on the year, the KRX is down 20.5% and the MFX is off 44.6%.

From an IV perspective (and yes, many of these companies could use some intravenous fluids) I generally glance at XLF only as a generic overview of the financial sector. The first finding of interest is that implied volatility in the XBD peaked in August and made a double top before Thanksgiving. This is consistent with the widespread belief that Goldman Sachs (GS) has dodged the subprime bullet and other players in this sector have had sufficient time and corporate agility – if not perhaps the ideal risk management policies – to limit any additional damage.

The banks are another story. Implied volatility in the money center banks and regional banks topped out at the end of November and is currently just below the August highs. Still more concerning, if not more surprising, is the performance of the mortgage finance sector, where implied volatility is above the August peak and in the process of challenging the late November high water mark. If I were a meteorologist looking at implied volatility, I would conclude that the storm has passed in the broker-dealer sector, but more thunderclouds are approaching in the regional banking and mortgage finance sectors.

Watch XBD’s Implied Volatility

With the DJIA up almost 100 points right out of the gate, I was curious to see GS and BSC quickly fade from green to red – and that weakness reflected in the XBD (Broker/Dealer Index.)

Of course, you probably don’t have to hedge the entire market too catastrophe-proof your portfolio these days. You can probably accomplish the same task by erecting a safety net under just one or two sectors, such as the home builders or financial institutions. So I looked at my favorite bellwether, Goldman Sachs, to see how their implied volatility has fared in the past month or so. While it makes for an interesting visual, I have not included the Goldman chart because the company has a history of slipping punches. A better chart is the XBD, whose components include 12 companies in the broker-dealer space.

The iVolatility chart below shows implied and historical volatility for the XBD going back three months. Prior to July, the XBD IV spent 95% of the past year in a narrow 20-25 range. After topping 50 in mid-August, the XBD IV looked to be headed back down to 30 or so, until the recent spike left it over 40 yesterday.

While it is important to watch the price level of this index to see how it holds up at support levels such as 215 and 208, I also recommend keeping a weather eye on the XBD’s implied volatility to see what the trend and absolute levels of IV tell us about the thinking of options players. It is quite likely that the tip of the next iceberg may be found floating in the IV chart before it shows up on a price chart.

Individual Stock Volatility: InterOil Corp. (IOC)

I have thus far resisted the temptation to talk about individual stocks and their associated volatility, but with all the crazy action in InterOil Corp. (IOC) right in the middle of options expiration week, this seems like the perfect opportunity to break the silence.

In a nutshell, the InterOil story is of a massive natural gas find (perhaps 15 trillion cubic feet) with their Elk-1 well, which was announced about a year ago. Current drilling in Elk-2 is ongoing and speculation about the results of these efforts is what is responsible for the extreme volatility in this stock – enough to take the stock down 60% in three days.

I have appended a chart of the implied and historical volatilities below, as well as a current snapshot of the options. To put it mildly, these options offer some unusual and interesting possibilities, given that expiration is only two days away, volatility is sky high, and the likelihood of a significant news announcement during that period appears to be slim. To further sweeten the pot, IOC just happens to be trading at exactly the 22.50 strike as I type this, which makes short straddle, short strangle and butterfly possibilities particularly interesting.

Those thinking about selling volatility for the next two days, however, should give some serious consideration to hedging their exposure by considering an iron butterfly or an iron condor. This is a keg of dynamite, after all, but there is a good possibility it will not go off in the next two days.

Implied Volatility and Earnings Spikers

A reader asked for some more details about how I use implied volatility (IV) and/or historical volatility to help identify companies with a high potential to spike following an earnings announcement. Specifically, she was looking at the 17% gains in Schnitzer Steel (SCHN) today and wondering if IV could have tipped her off to the probability of a big move.

Regular readers will probably recall that I beat the earnings spiker horse rather severely during the ill-fated (more on their part than mine) CNBC Million Dollar Portfolio Challenge, but for anyone who is interested in some details, I laid out the bulk of my thinking in “How to Find the Spiker Before the Earnings Announcement.”

Since part of the query touched on why I thought IV was better than beta for determining volatility (past and future), here are three reasons why I think IV is superior to beta:

• Yahoo Finance, Google Finance, and other data providers sometimes list betas of 1.0 for issues they apparently have not calculated a beta for, particularly newer issues and foreign stocks
• highly volatile stocks that go in the opposite direction of the market for awhile can sometimes have low betas -- think small oil/gas exploration companies, gold miners, etc., but also consider that some tech stocks may countertrend for a long period and thus acquire a smaller beta than their volatility would suggest (historical volatility would be a better number to watch here, because it focuses entirely on the magnitude of the moves and does not care whether these moves are correlated to the broader markets)
• implied volatility is forward looking, so it automatically adjusts to account for scheduled earnings announcements, a pending FDA drug decision, a legal issue that is due to be resolved, buyout rumors, terrorism, violence in the Middle East, a hurricane that is bearing down on the US, etc.

Getting to the meat of the question, since I am looking forward in time to earnings, I pretty much ignore historical volatility and focus entirely on IV. I usually try to target the top 10% or so most volatile companies that are due to report in a 24 hour period, so that if it is mid-June and there are very few reports, I might look at IV as low as 35 (I generally consider 40 to be a minimum IV), but by the end of July to early August earnings peak, there will be so many small and extremely volatile companies reporting that it might be possible to screen out all companies with an IV below 50. Also, once you get over about 60 or so, I am not sure that a higher IV really translates to incremental future volatility in the short-term (unless we stray from earnings and talk about FDA decisions, etc.)

Regarding specific numbers, I use the current IV Index call number (the 44.20% from the SCHN iVolatility link), but the current IV Index put and current IV Index mean are usually so closely correlated that it doesn't matter which one you pick -- as long as it is a current number.

I also recommend that readers consider looking not just at the raw numbers but also at the 12 month volatility charts (such as this iVolatility chart for SCHN) where it is often much easier to visualize the size of the current pre-earnings volatility run-up – and also compare it to similar up-trending volatility patterns that preceded earnings in the past few quarters. In the case of SCHN, you can see a big jump in volatility during the past week and a sustained move since about mid-April. Implied volatility may not have been screaming “Buy!” in an unambiguous manner, but one can reasonably argue that at an 11 month high just prior to this morning’s earnings announcement, it was warning of the increased possibility of a big move in one direction or the other.

Finally, I would be would be remiss in not reiterating that directional earnings plays are highly speculative and usually carry a formidable risk/reward profile, so I recommend that anyone who plays the earnings lottery considers limiting their exposure with an options play or by making a bet on volatility instead of direction, as volatility typically – and much more predictably – decreases 15-20% the day after earnings are announced.

VIX Implied Volatility at a 52 Week Low

There is always a risk of trying to cram too much information into one graphic, but with the image to the right, I figured it was worth a try.

The chart, which comes courtesy of optionsXpress, depicts implied and historical volatility in VIX options over the past year, in addition to the VIX price, which is part of the reason why it is messier than the (more elegant and readable) iVolatility VIX options chart that shows only implied and historical volatility.

The reason I bother mentioning any of this is that VIX call options closed yesterday with their lowest implied volatility reading of the past 52 weeks. So if you think the market is getting toppy, but you are reluctant to go long the VIX because it is still a fair distance from single digits, consider that the volatility premium for VIX calls is as cheap as it has been in a long time.

IV and HV for SPY and VIX

Earlier today, Adam at Daily Options Report posted a one year chart of the implied and historical volatility for SPY (commonly known as SPDRs or Spiders, the original ETF used to track the S&P 500 index) to help drive home the point that for all but one of the past eight and a half months, the SPY options have been overpriced relative to the volatility of the underlying. Adam’s bottom line is that the gap between the current IV and historical IV for SPY is at least as wide as it has been during the past year.

Since I have not done so before, I thought it might be instructive to juxtapose (my favorite word, but I digress…) the identical VIX chart to see if any related conclusions might jump off of the page.

In looking at the charts below, at least four different conclusions immediately present themselves to my eye:

• unlike SPY, the VIX IV has, on average, tracked reasonably close to historical volatility over the course of the past year; with the exception of brief spikes, only in June-July and September-October (in the pre-2/27 world) were there significant enduring discrepancies
  
  
• the current VIX IV and HV are almost identical, in sharp contrast to the wide spread in the SPDRs
  
  
• since mid-April the SPY IV has risen noticeably, while the VIX IV has continued to drop
  
  
• partly as a consequence of the above, while the SPY IV is close to the middle of its one year range, the VIX IV is at the bottom of the range, approaching a one year low

I am not sure what to make of these discrepancies at the moment, but I thought I would pass along my observations and invite reader contemplation and comment. In thinking about the VIX vs. the SPY, keep in mind that VIX options are based off of futures and consequently have a slightly different time horizon than SPY options.

How to Find the Spiker Before the Earnings Announcement

Several readers have expressed interest in how I came up with my earnings spike potential algorithm. Essentially, this is something that has evolved over the past three weeks as a result of my desire to find companies with a high probability of making a substantial near-term move and give my CNBC Million Dollar Portfolio Challenge portfolio a chance to make a run at the finals. To make a long story short, I got the volatility I wanted, but I didn’t always get the direction right.

I would not call this a battle-tested formula. It is more like a hypothesis that continues to evolve as I get more data and continue to test and tune some of the elements. Think of it as just-in-time sausage making.

I am not an arsonist (I even missed out on youthful pyromania), but I liken this task to understanding how to get a fire started and make sure it quickly builds in intensity and spreads as rapidly as possible. For a fire, you need a starter and an accelerant; for an earnings spike, it’s essentially the same thing.

In the links below, wherever possible I have provided a favorite deep link to a free public source that includes the relevant data, calculation, graphic, etc.

Some of the more important factors I look at are:

• Implied volatility, a great initial screening tool (higher is better) – free data at iVolatility.com; if you have an account at optionsXpress, they have excellent options screens available to all
  
  
• Beta (higher is better) is another good accelerant barometer, though not as good as IV – available many places, including Google Finance
  
  
• Number of analysts (lower is better) and degree of analyst consensus (lower is usually better) – Marketwatch.com has a page that not only summarizes the analyst estimates, but also provides a “coefficient variance” number that gives you a sense of the dispersion of opinion. In many cases, the earnings and/or revenue surprise is the fire starter.
  
  
• Short ratio: days to cover (higher is better) – a classic accelerant indicator, with free data available at ShortSqueeze.com

Some secondary factors to consider:

• Price to earnings ratio (negative or n/a is best, higher is better) – available many places, including Google Finance
  
  
• Earnings history data – there is a higher probability of a surprise if there is an erratic earnings history; there is also greater potential for a high magnitude surprise if there is a consistent pattern of beating (or missing) expectations – assuming the pattern can be broken. One fun source that has earnings dates baked in to charts and post-earnings performance data available is WhisperNumber.com (more complete data for larger companies.)
  
  
• Recent analyst ranking and/or price estimate changes (none is best) – these can work both ways, but most often they reduce the probability of a surprise. Again, Marketwatch.com is a good source.
  
  
• News flow – this is a highly subjective/qualitative assessment, but there are certain types of pre-earnings news that I believe can indicate in increased or decreased likelihood of an earnings surprise. Be particularly wary of binary events, such as the pending FDA approval for a drug and the like. The best place to find the relevant information is probably by looking at company news at Yahoo Finance. I am not ready to expand upon this one at this stage, except for…
  
  
• Recent company guidance (none is best) – as with recent changes in analyst opinion, these usually dampen the surprise potential. Again, try Yahoo Finance.
  
  
• Insider transactions – these are sometimes difficult to evaluate in the context of earnings, but if an apparent transactional pattern is confirmed or contradicted by earnings, there could be an accelerant. I favor Form4Oracle as a free public source of insider transaction data.
  
  
• Technical analysis – this is good for identifying the heightened possibility of breakouts, violation of important support and resistance levels, and other factors that may act as technical accelerants. The gallery view at StockCharts.com is always a good place to start.
  
  
• Put to call ratio (higher is better) – somewhat analogous to the short data is the individual stock open interest put to call ratio, data for which is available at SchaeffersResearch.com
  
  
• Recent options activity – another subjective and difficult to assess measure, but if significant changes in open interest favor either puts or calls, this may be a tell. Not much in the way of great public data, but you may get some valuable information from Yahoo Finance.
  
  
• Company size (lower is better) – this includes revenues and market capitalization. Available many places, including Google Finance.
  
  
• Recent IPO or lack of relevant operating history (less history is better) – In general, the shorter the track record, the bigger the chance for an earnings surprise. This means that the first quarterly report or two with new management, new products, a new acquisition, etc. increases uncertainty about the result – and the potential for a surprise.

As a footnote, if you are looking for a good source for who reports when that is sortable by time of day (BMO, AMC, etc.), I like TheStreet.com’s Earnings Release calendar, where you can click on the Date/Time column to sort accordingly. If you are not familiar with a lot of the tickers/companies, then I suggest that a first pass be limited to those companies with four letter tickers whose EPS estimate and/or previous year actual EPS is negative or close to zero.

Finally, I feel obliged to remind everyone that this is a method for finding the high potential post-earnings movers, *not* the winners. I continue to play with the weightings of the various factors and ultimately your weightings should reflect your research and beliefs about the market. If you keep track of the pre-earnings data and the outcomes, you should be able to develop and tweak your own model – or at least flag some potential high fliers.

VIX Implied Volatility Falls Below Pre-2/27 Level

The last time I mentioned the widening gap between the VIX's implied volatility and historical volatility, IV stood at 85, well below the average IV reading for the past year. In the two weeks since that post, IV has fallen all the way down to 75, which is now below even pre-2/27 levels, as the graph below demonstrates.

I am not sure what to make of this other than to observe that we appear to be entering another period of meta-complacency. Though the VWSI is not flashing a signal to buy the VIX yet, this does look like it might be a good time to start nibbling.